Citation :

Nitin Sonaji Magar, Zafar Ul Hasan, Anand B.Humbe, 2023. "Design and Development of Optimized Cardiovascular Disease Prediction Model using Artificial Intelligence" ESP International Journal of Advancements in Science & Technology (ESP-IJAST)  Volume 1, Issue 2: 20-28.

Abstract :

The medical industry is expanding quickly as new ailments are discovered daily, necessitating the development of effective treatment options. The muscular heart, which is about the size of a clenched human fist, is in charge of blood circulation. Although heart/cardiac disease is the term used to describe conditions that generally affect the heart, there are numerous conditions that fall under this umbrella term, including coronary artery disease (CAD), cardiomyopathy, cardiovascular disease (CVD), and others that depend on blood flow throughout the body. The heart disease data prediction has been made to analyse medical data with clinical expertise in order to assist clinicians in the diagnosis of heart disease. The accuracy of heart disease diagnostic decisions can be improved by the development of these predictive algorithms. The prognosis of heart illness depends heavily on data mining. The Naive Bayes (NB), C4.5, and Artificial Neural Network (ANN)-Back Propagation (BP) methods are employed in this work. These age-old techniques are used to forecast cardiac disease. The NB classifier approach, which is based on the Bayesian theorem, is employed when the input's dimensionality is extremely high. It performs better than other protocols despite being straightforward. The C4.5 protocol use data entropy perception to create decision trees from a training data set. It is a widely used procedure also referred to as the statistical classifier. ANN has been used as a method for resolving a variety of decision modelling issues in common situations. Modelling, pattern recognition, data processing, and sequence recognition systems are examples of applications where ANNs are used.

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Keywords :

Classifier, Swarm Optimization, AI, Cardiovascular Disease, ANN.